AIMC Topic: Multiple Sclerosis

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Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

IEEE transactions on medical imaging
We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that co...

Multiscale entropy identifies differences in complexity in postural control in women with multiple sclerosis.

Gait & posture
Loss of postural center-of-pressure complexity (COP complexity) has been associated with reduced adaptability that accompanies disease and aging. The aim of this study was to identify if COP complexity is reduced: (1) in those with Multiple Sclerosis...

Robot-supported upper limb training in a virtual learning environment : a pilot randomized controlled trial in persons with MS.

Journal of neuroengineering and rehabilitation
BACKGROUND: Despite the functional impact of upper limb dysfunction in multiple sclerosis (MS), effects of intensive exercise programs and specifically robot-supported training have been rarely investigated in persons with advanced MS.

Classification of multiple sclerosis lesions using adaptive dictionary learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task,...

Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality ...

Robotic-assisted gait training in neurological patients: who may benefit?

Annals of biomedical engineering
Regaining one's ability to walk is of great importance for neurological patients and is a major goal of all rehabilitation programs. Gait training of severely affected patients after the neurological event is technically difficult because of their mo...

Predicting outcome in clinically isolated syndrome using machine learning.

NeuroImage. Clinical
We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clini...

Transfer learning improves supervised image segmentation across imaging protocols.

IEEE transactions on medical imaging
The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervis...

PSR-MAPMS: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis using multi-source propensity scores.

Protein science : a publication of the Protein Society
Within the central nervous system, the myelin sheath is composed of elements known as myelin autoantigens that are mistakenly targeted by the immune system in multiple sclerosis (MS). This autoimmune attack leads to the destruction of myelin, resulti...